from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_SCREAMING_SNAKE_CASE 		=      8





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=BITS       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      x.device

 UpperCamelCase			  =      (x * 255).int().clamp(0							,					255       )

 UpperCamelCase			  =      2 ** torch.arange(bits - 1							,					-1							,					-1							,					device=UpperCamelCase_       )
 UpperCamelCase			  =      rearrange(UpperCamelCase_							,					"""d -> d 1 1"""       )
 UpperCamelCase			  =      rearrange(UpperCamelCase_							,					"""b c h w -> b c 1 h w"""       )

 UpperCamelCase			  =      ((x & mask) != 0).float()
 UpperCamelCase			  =      rearrange(UpperCamelCase_							,					"""b c d h w -> b (c d) h w"""       )
 UpperCamelCase			  =      bits * 2 - 1
 return bits





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=BITS       )		->	Dict:







 '''simple docstring'''
 UpperCamelCase			  =      x.device

 UpperCamelCase			  =      (x > 0).int()
 UpperCamelCase			  =      2 ** torch.arange(bits - 1							,					-1							,					-1							,					device=UpperCamelCase_							,					dtype=torch.intaa       )

 UpperCamelCase			  =      rearrange(UpperCamelCase_							,					"""d -> d 1 1"""       )
 UpperCamelCase			  =      rearrange(UpperCamelCase_							,					"""b (c d) h w -> b c d h w"""							,					d=8       )
 UpperCamelCase			  =      reduce(x * mask							,					"""b c d h w -> b c h w"""							,					"""sum"""       )
 return (dec / 255).clamp(0.0							,					1.0       )





def 			lowercase(	self							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_ = 0.0							,					UpperCamelCase_ = True							,					UpperCamelCase_=None							,					UpperCamelCase_ = True							,					)		->	Union[DDIMSchedulerOutput, Tuple]:







 '''simple docstring'''
 if self.num_inference_steps is None:
  raise ValueError(
      """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""       )

 # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
 # Ideally, read DDIM paper in-detail understanding

 # Notation (<variable name> -> <name in paper>
 # - pred_noise_t -> e_theta(x_t, t)
 # - pred_original_sample -> f_theta(x_t, t) or x_0
 # - std_dev_t -> sigma_t
 # - eta -> η
 # - pred_sample_direction -> "direction pointing to x_t"
 # - pred_prev_sample -> "x_t-1"

 # 1. get previous step value (=t-1)
 UpperCamelCase			  =      timestep - self.config.num_train_timesteps // self.num_inference_steps

 # 2. compute alphas, betas
 UpperCamelCase			  =      self.alphas_cumprod[timestep]
 UpperCamelCase			  =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

 UpperCamelCase			  =      1 - alpha_prod_t

 # 3. compute predicted original sample from predicted noise also called
 # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
 UpperCamelCase			  =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

 # 4. Clip "predicted x_0"
 UpperCamelCase			  =      self.bit_scale
 if self.config.clip_sample:
  UpperCamelCase			  =      torch.clamp(UpperCamelCase_							,					-scale							,					UpperCamelCase_       )

 # 5. compute variance: "sigma_t(η)" -> see formula (16)
 # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
 UpperCamelCase			  =      self._get_variance(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      eta * variance ** 0.5

 if use_clipped_model_output:
  # the model_output is always re-derived from the clipped x_0 in Glide
  UpperCamelCase			  =      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

 # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
 UpperCamelCase			  =      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

 # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
 UpperCamelCase			  =      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

 if eta > 0:
  # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
  UpperCamelCase			  =      model_output.device if torch.is_tensor(UpperCamelCase_       ) else """cpu"""
  UpperCamelCase			  =      torch.randn(model_output.shape							,					dtype=model_output.dtype							,					generator=UpperCamelCase_       ).to(UpperCamelCase_       )
  UpperCamelCase			  =      self._get_variance(UpperCamelCase_							,					UpperCamelCase_       ) ** 0.5 * eta * noise

  UpperCamelCase			  =      prev_sample + variance

 if not return_dict:
  return (prev_sample,)

 return DDIMSchedulerOutput(prev_sample=UpperCamelCase_							,					pred_original_sample=UpperCamelCase_       )





def 			lowercase(	self							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_="epsilon"							,					UpperCamelCase_=None							,					UpperCamelCase_ = True							,					)		->	Union[DDPMSchedulerOutput, Tuple]:







 '''simple docstring'''
 UpperCamelCase			  =      timestep

 if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
  UpperCamelCase  ,     UpperCamelCase			  =      torch.split(UpperCamelCase_							,					sample.shape[1]							,					dim=1       )
 else:
  UpperCamelCase			  =      None

 # 1. compute alphas, betas
 UpperCamelCase			  =      self.alphas_cumprod[t]
 UpperCamelCase			  =      self.alphas_cumprod[t - 1] if t > 0 else self.one
 UpperCamelCase			  =      1 - alpha_prod_t
 UpperCamelCase			  =      1 - alpha_prod_t_prev

 # 2. compute predicted original sample from predicted noise also called
 # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
 if prediction_type == "epsilon":
  UpperCamelCase			  =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
 elif prediction_type == "sample":
  UpperCamelCase			  =      model_output
 else:
  raise ValueError(f"""Unsupported prediction_type {prediction_type}."""       )

 # 3. Clip "predicted x_0"
 UpperCamelCase			  =      self.bit_scale
 if self.config.clip_sample:
  UpperCamelCase			  =      torch.clamp(UpperCamelCase_							,					-scale							,					UpperCamelCase_       )

 # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
 # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
 UpperCamelCase			  =      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
 UpperCamelCase			  =      self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

 # 5. Compute predicted previous sample µ_t
 # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
 UpperCamelCase			  =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

 # 6. Add noise
 UpperCamelCase			  =      0
 if t > 0:
  UpperCamelCase			  =      torch.randn(
      model_output.size()							,					dtype=model_output.dtype							,					layout=model_output.layout							,					generator=UpperCamelCase_       ).to(model_output.device       )
  UpperCamelCase			  =      (self._get_variance(UpperCamelCase_							,					predicted_variance=UpperCamelCase_       ) ** 0.5) * noise

 UpperCamelCase			  =      pred_prev_sample + variance

 if not return_dict:
  return (pred_prev_sample,)

 return DDPMSchedulerOutput(prev_sample=UpperCamelCase_							,					pred_original_sample=UpperCamelCase_       )


class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def __init__(     self						:						str   ,   lowerCamelCase_						:						UNetaDConditionModel   ,   lowerCamelCase_						:						Union[DDIMScheduler, DDPMScheduler]   ,   lowerCamelCase_						:						Optional[float] = 1.0   ,   ):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      bit_scale
    UpperCamelCase			  =      (
        ddim_bit_scheduler_step if isinstance(lowerCamelCase_   ,   lowerCamelCase_				) else ddpm_bit_scheduler_step
    )

    self.register_modules(unet=lowerCamelCase_   ,   scheduler=lowerCamelCase_				)







   @torch.no_grad()
   def __call__(     self						:						Dict   ,   lowerCamelCase_						:						Optional[int] = 256   ,   lowerCamelCase_						:						Optional[int] = 256   ,   lowerCamelCase_						:						Optional[int] = 50   ,   lowerCamelCase_						:						Optional[torch.Generator] = None   ,   lowerCamelCase_						:						Optional[int] = 1   ,   lowerCamelCase_						:						Optional[str] = "pil"   ,   lowerCamelCase_						:						bool = True   ,   **lowerCamelCase_						:						Optional[Any]   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      torch.randn(
        (batch_size, self.unet.config.in_channels, height, width)   ,   generator=lowerCamelCase_   ,   )
    UpperCamelCase			  =      decimal_to_bits(lowerCamelCase_				) * self.bit_scale
    UpperCamelCase			  =      latents.to(self.device				)

    self.scheduler.set_timesteps(lowerCamelCase_				)

    for t in self.progress_bar(self.scheduler.timesteps				):
     # predict the noise residual
     UpperCamelCase			  =      self.unet(lowerCamelCase_   ,   lowerCamelCase_				).sample

     # compute the previous noisy sample x_t -> x_t-1
     UpperCamelCase			  =      self.scheduler.step(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				).prev_sample

    UpperCamelCase			  =      bits_to_decimal(lowerCamelCase_				)

    if output_type == "pil":
     UpperCamelCase			  =      self.numpy_to_pil(lowerCamelCase_				)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=lowerCamelCase_				)

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


_SCREAMING_SNAKE_CASE 		=      ["""text""", """image""", """audio"""]





def 			lowercase(	UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for input_type in input_types:
  if input_type == "text":
   inputs.append("""Text input"""       )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       ).resize((512, 512)       )       )
  elif input_type == "audio":
   inputs.append(torch.ones(3000       )       )
  elif isinstance(UpperCamelCase_							,					UpperCamelCase_       ):
   inputs.append(create_inputs(UpperCamelCase_       )       )
  else:
   raise ValueError(f"""Invalid type requested: {input_type}"""       )

 return inputs





def 			lowercase(	UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for output in outputs:
  if isinstance(UpperCamelCase_							,					(str, AgentText)       ):
   output_types.append("""text"""       )
  elif isinstance(UpperCamelCase_							,					(Image.Image, AgentImage)       ):
   output_types.append("""image"""       )
  elif isinstance(UpperCamelCase_							,					(torch.Tensor, AgentAudio)       ):
   output_types.append("""audio"""       )
  else:
   raise ValueError(f"""Invalid output: {output}"""       )

 return output_types


@is_tool_test
class SCREAMING_SNAKE_CASE_		:







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """inputs"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """outputs"""				)				)

    UpperCamelCase			  =      self.tool.inputs
    for _input in inputs:
     if isinstance(_input   ,   lowerCamelCase_				):
      for __input in _input:
       self.assertTrue(__input in authorized_types				)
     else:
      self.assertTrue(_input in authorized_types				)

    UpperCamelCase			  =      self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    # There is a single output
    if len(self.tool.outputs				) == 1:
     UpperCamelCase			  =      [outputs]

    self.assertListEqual(output_types(lowerCamelCase_				)   ,   self.tool.outputs				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """description"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """default_checkpoint"""				)				)
    self.assertTrue(self.tool.description.startswith("""This is a tool that"""				)				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)

    for output, output_type in zip(lowerCamelCase_   ,   self.tool.outputs				):
     UpperCamelCase			  =      AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)

    UpperCamelCase			  =      []

    for _input, input_type in zip(lowerCamelCase_   ,   self.tool.inputs				):
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)
